The Effects of Detector Spacing on Travel Time Prediction on Freeways
Loop detectors report traffic characteristics in real
time. They are at the core of traffic control process. Intuitively,
one would expect that as density of detection increases, so would
the quality of estimates derived from detector data. However, as
detector deployment increases, the associated operating and
maintenance cost increases. Thus, traffic agencies often need to
decide where to add new detectors and which detectors should
continue receiving maintenance, given their resource constraints.
This paper evaluates the effect of detector spacing on freeway
travel time estimation. A freeway section (Interstate-15) in Salt
Lake City metropolitan region is examined. The research reveals
that travel time accuracy does not necessarily deteriorate with
increased detector spacing. Rather, the actual location of detectors
has far greater influence on the quality of travel time estimates.
The study presents an innovative computational approach that
delivers optimal detector locations through a process that relies on
Genetic Algorithm formulation.
[1] D. Bremmer, K. Cotton, D. Cotey, and C. Prestrud, "Measuring
congestion: learning from operational data," Transportation Research
Record, Transportation Research Board of the National Academies, No.
1895, 2004, pp. 186-196.
[2] A. Massey, G. W. Saylor, H. W. Wood, B. Baur, and E. Hauser,
"Summary of ITS best management practices and technologies for the
state of ohio," in ASCE Conference Proceedings, October 2001, pp.127-
134.
[3] J. Kwon, K. Petty, and P. Varaiya, "Probe vehicle runs or loop
detectors? Effect of detector spacing and sample size on the accuracy of
freeway congestion monitoring," Presented at 86th Annual Meeting of
the Transportation Research Board, Washington, D.C., January 2006.
[4] K. Ozbay, B. Bartin, and S. Chien, "South Jersey real-time motorist
information system: technology and practice," Transportation Research
Record, Transportation Research Board of the National Academies, No.
1886, 2004, pp. 68-75.
[5] B. Bartin, K. Ozbay, and C. Iyigun, "A clustering based methodology
for determining the optimal roadway configuration of detectors for travel
time estimation," Transportation Research Record, Transportation
Research Board National Research Council, No. 2000, 2007, pp. 98-105.
[6] I. Fujito, R. Margiolta, W. Huang, and W. A. Perez, "Effect of detector
spacing on performance measure calculations," Transportation
Research Record, No. 1945, Transportation Research Board of the
National Academies, 2006, pp. 1-11.
[7] K. S. Chan, and W. H. K. Lam, " Optimal speed detector density for the
network with travel time information," Transportation Research
Record, Transportation Research Board of the National Academies,
No 36, 2002, pp. 203-223.
[8] A. Sen, P. Thankuriah, X. Zhu, and A. Karr, "Frequency of probe
reports and variance of travel time estimates," Journal of Transportation
Engineering, ASCE, Vol. 123, No. 4, 1997, pp. 290-297.
[9] X. Ban, L. Chu, and H. Benouar, "Bottleneck identification and
calibration for corridor management planning," Transportation
Research Record, Transportation Research Board of the National
Academies, No. 1999, 2007, pp. 40-53.
[10] H. Chen, M. S. Dougherty, and H. R. Kirby, "The effects of detector
spacing on traffic forecasting," Computer-Aided Civil and Infrastructure
Engineering, Volume 16, No. 6, 2001, pp. 422- 430.
[11] R. Cheu, and S. Ritchie, "Automated detection of lane-blocking freeway
incidents using artificial neural networks," Transportation Research
Record, Part C, Volume 3, Issue 6, December 1995, pp. 371-388.
[12] Goldberg, D. E, Genetic Algorithms in Search, Optimization and
Machine Learning. Reading, MA: Addison-Wesley, 1989, pp. 1-32.
[13] K. Deb, Multi-Objective Optimization using Evolutionary Algorithms,
John Wiley & Sons, 2001, pp. 13-45.
[14] VISSIM 4.30 Manual. Planung Transport Verkehr (PTV) AG., 2007.
[15] P.T. Martin, A. Stevanovic, I. Vladisavljevic, and D. Jovanovic.
"VISUM-Online (OTACHAT)," University of Utah Traffic Laboratory,
Salt Lake City, Utah, UTL-1106-90, January 2007.
[16] Travel Time Data Collection Handbook, FHWA-PL-98-035, TTI, Texas
A&M University, Texas, 1998.
[17] P.T. Martin, I. Vladisavljevic, and D. Yusufzyanova, "The I-15 Express
lanes evaluation", University of Utah Traffic Laboratory, Salt Lake City,
Utah, UTL-1106- 89, November 2007, vol 9.
[18] P.T. Martin, I. Vladisavljevic, J. Ries, and B. Nadimpalli, "Express lane
genetic algorithm microsimulation evaluation part 2", University of Utah
Traffic Laboratory, Salt Lake City, Utah, UTL- 02- 08- 96, November
2008.
[19] KMZ file provided by UDOT.
[20] P. Edara, J. Guo, B. L. Smith, and C. McGhee, "Optimal placement of
point detectors on Virginia-s freeways: case studies of northern Virginia
and Richmond," Virginia Transportation Research Council, Virginia
Department of Transportation, VTRC 08-CR3, January 2008.
[21] C.S. ReVelle, and Eiselt, H.A, "Location analysis: a synthesis and
survey," European Journal of Operational Research, Vol. 165, 2005, pp.
1-19.
[22] S. Chan, and H.K. Lam, "Optimal speed detector density for the network
with travel time information," Transportation Research A, Vol. 36,
2002, pp. 203-223.
[23] A. Ehlert, M. Bell, and S. Grosso, "The optimization of traffic count
locations in road networks," Transportation Research Record, Part B,
Vol. 40, 2006, pp. 460-479.
[24] M. Gen, and R. Cheng, Genetic Algorithms & Engineering
Optimization, New York: John Wiley and Sons, 2000, ch.2.
[25] K. Deb, A. Pratap, S. Argawal, and T. Meyarivan, "A fast elitist nondominated
sorting genetic algorithm for multi-objective optimization:
NSGA-II," Int conference on parallel problem solving from nature No.6,
Paris, France, 2000, vol. 1917, pp. 849-858.
[26] K. Deb, A. Pratap, S. Argawal, and T. Meyarivan, "A fast and elitist
multiobjective genetic algorithm: NSGA-II," IEEE Transactions on
Evolutionary Computation, Vol. 6, 2002, pp.182-197.
[27] P. Ngatchou, Z. Anahita and M.A. El-Sharkawi, "Pareto multi objective
optimization," in Proc. of the 13th International Conference on
Intelligent Systems Application to Power Systems, Nov. 2005, pp. 84-91.
[1] D. Bremmer, K. Cotton, D. Cotey, and C. Prestrud, "Measuring
congestion: learning from operational data," Transportation Research
Record, Transportation Research Board of the National Academies, No.
1895, 2004, pp. 186-196.
[2] A. Massey, G. W. Saylor, H. W. Wood, B. Baur, and E. Hauser,
"Summary of ITS best management practices and technologies for the
state of ohio," in ASCE Conference Proceedings, October 2001, pp.127-
134.
[3] J. Kwon, K. Petty, and P. Varaiya, "Probe vehicle runs or loop
detectors? Effect of detector spacing and sample size on the accuracy of
freeway congestion monitoring," Presented at 86th Annual Meeting of
the Transportation Research Board, Washington, D.C., January 2006.
[4] K. Ozbay, B. Bartin, and S. Chien, "South Jersey real-time motorist
information system: technology and practice," Transportation Research
Record, Transportation Research Board of the National Academies, No.
1886, 2004, pp. 68-75.
[5] B. Bartin, K. Ozbay, and C. Iyigun, "A clustering based methodology
for determining the optimal roadway configuration of detectors for travel
time estimation," Transportation Research Record, Transportation
Research Board National Research Council, No. 2000, 2007, pp. 98-105.
[6] I. Fujito, R. Margiolta, W. Huang, and W. A. Perez, "Effect of detector
spacing on performance measure calculations," Transportation
Research Record, No. 1945, Transportation Research Board of the
National Academies, 2006, pp. 1-11.
[7] K. S. Chan, and W. H. K. Lam, " Optimal speed detector density for the
network with travel time information," Transportation Research
Record, Transportation Research Board of the National Academies,
No 36, 2002, pp. 203-223.
[8] A. Sen, P. Thankuriah, X. Zhu, and A. Karr, "Frequency of probe
reports and variance of travel time estimates," Journal of Transportation
Engineering, ASCE, Vol. 123, No. 4, 1997, pp. 290-297.
[9] X. Ban, L. Chu, and H. Benouar, "Bottleneck identification and
calibration for corridor management planning," Transportation
Research Record, Transportation Research Board of the National
Academies, No. 1999, 2007, pp. 40-53.
[10] H. Chen, M. S. Dougherty, and H. R. Kirby, "The effects of detector
spacing on traffic forecasting," Computer-Aided Civil and Infrastructure
Engineering, Volume 16, No. 6, 2001, pp. 422- 430.
[11] R. Cheu, and S. Ritchie, "Automated detection of lane-blocking freeway
incidents using artificial neural networks," Transportation Research
Record, Part C, Volume 3, Issue 6, December 1995, pp. 371-388.
[12] Goldberg, D. E, Genetic Algorithms in Search, Optimization and
Machine Learning. Reading, MA: Addison-Wesley, 1989, pp. 1-32.
[13] K. Deb, Multi-Objective Optimization using Evolutionary Algorithms,
John Wiley & Sons, 2001, pp. 13-45.
[14] VISSIM 4.30 Manual. Planung Transport Verkehr (PTV) AG., 2007.
[15] P.T. Martin, A. Stevanovic, I. Vladisavljevic, and D. Jovanovic.
"VISUM-Online (OTACHAT)," University of Utah Traffic Laboratory,
Salt Lake City, Utah, UTL-1106-90, January 2007.
[16] Travel Time Data Collection Handbook, FHWA-PL-98-035, TTI, Texas
A&M University, Texas, 1998.
[17] P.T. Martin, I. Vladisavljevic, and D. Yusufzyanova, "The I-15 Express
lanes evaluation", University of Utah Traffic Laboratory, Salt Lake City,
Utah, UTL-1106- 89, November 2007, vol 9.
[18] P.T. Martin, I. Vladisavljevic, J. Ries, and B. Nadimpalli, "Express lane
genetic algorithm microsimulation evaluation part 2", University of Utah
Traffic Laboratory, Salt Lake City, Utah, UTL- 02- 08- 96, November
2008.
[19] KMZ file provided by UDOT.
[20] P. Edara, J. Guo, B. L. Smith, and C. McGhee, "Optimal placement of
point detectors on Virginia-s freeways: case studies of northern Virginia
and Richmond," Virginia Transportation Research Council, Virginia
Department of Transportation, VTRC 08-CR3, January 2008.
[21] C.S. ReVelle, and Eiselt, H.A, "Location analysis: a synthesis and
survey," European Journal of Operational Research, Vol. 165, 2005, pp.
1-19.
[22] S. Chan, and H.K. Lam, "Optimal speed detector density for the network
with travel time information," Transportation Research A, Vol. 36,
2002, pp. 203-223.
[23] A. Ehlert, M. Bell, and S. Grosso, "The optimization of traffic count
locations in road networks," Transportation Research Record, Part B,
Vol. 40, 2006, pp. 460-479.
[24] M. Gen, and R. Cheng, Genetic Algorithms & Engineering
Optimization, New York: John Wiley and Sons, 2000, ch.2.
[25] K. Deb, A. Pratap, S. Argawal, and T. Meyarivan, "A fast elitist nondominated
sorting genetic algorithm for multi-objective optimization:
NSGA-II," Int conference on parallel problem solving from nature No.6,
Paris, France, 2000, vol. 1917, pp. 849-858.
[26] K. Deb, A. Pratap, S. Argawal, and T. Meyarivan, "A fast and elitist
multiobjective genetic algorithm: NSGA-II," IEEE Transactions on
Evolutionary Computation, Vol. 6, 2002, pp.182-197.
[27] P. Ngatchou, Z. Anahita and M.A. El-Sharkawi, "Pareto multi objective
optimization," in Proc. of the 13th International Conference on
Intelligent Systems Application to Power Systems, Nov. 2005, pp. 84-91.
@article{"International Journal of Architectural, Civil and Construction Sciences:55162", author = "Piyali Chaudhuri and Peter T. Martin and Aleksandar Z. Stevanovic and Chongkai Zhu", title = "The Effects of Detector Spacing on Travel Time Prediction on Freeways", abstract = "Loop detectors report traffic characteristics in real
time. They are at the core of traffic control process. Intuitively,
one would expect that as density of detection increases, so would
the quality of estimates derived from detector data. However, as
detector deployment increases, the associated operating and
maintenance cost increases. Thus, traffic agencies often need to
decide where to add new detectors and which detectors should
continue receiving maintenance, given their resource constraints.
This paper evaluates the effect of detector spacing on freeway
travel time estimation. A freeway section (Interstate-15) in Salt
Lake City metropolitan region is examined. The research reveals
that travel time accuracy does not necessarily deteriorate with
increased detector spacing. Rather, the actual location of detectors
has far greater influence on the quality of travel time estimates.
The study presents an innovative computational approach that
delivers optimal detector locations through a process that relies on
Genetic Algorithm formulation.", keywords = "Detector, Freeway, Genetic algorithm, Travel timeestimate.", volume = "4", number = "6", pages = "140-10", }
